Nowadays, the paper industry supplies its required fibers either from primary fibers, including wood and plants, or waste papers, called secondary fibers. One of the most challenging recycling processes is deinking of papers digitally printed with electrophotographic ink. In order to produce optically high-quality paper from recycled waste papers, deinking step is required at the desired levels. In this work, the environmentally friendly green enzymatic deinking of printed paper was modeled and optimized via an innovative approach called artificial intelligence method. The effect of treatment temperature, treatment time, and enzyme dosage on mechanical properties (tensile and burst strengths) as well as optical properties (whiteness and brightness) of handsheet was investigated. The developed code can appropriately learn the non-linear behavior of deinking process, and make decisions according to the pattern constructed intelligently. Finally, multi-objective optimization at the specified treatment temperature, treatment time, and enzyme dosage was performed to identify the best conditions for enzyme-deinked handsheet (maximized mechanical and optical properties).
Keywords: Artificial neural network; Electrophotography; Enzymatic deinking; Paper recycling; Toner.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.